Volume 13 | Issue 4 |

 Abstract

The Startup-Investor Connecting Platform is an innovative digital solution designed to bridge the gap between startups and investors, fostering a vibrant ecosystem of entrepreneurial growth and collaboration. Built using modern web technologies like Next.js (Canary version), TypeScript, Tailwind CSS, ShadCN, and powered by Sanity for content management, the platform offers a responsive, secure, and scalable user interface.Startups can create detailed profiles to showcase their ideas, while investors benefit from powerful search and filtering capabilities--enabling them to discover startups based on founders, categories, or specific keywords. A special "Related Startups" section helps users explore similar ventures, promoting wider discovery and increased engagement across the platform.To facilitate professional networking, the platform includes a "Connect Investors" feature, which links directly to investors' LinkedIn profiles, making it easier to initiate meaningful collaborations. With GitHub authentication integrated via NextAuth, the platform ensures strong data protection and smooth user access.By combining advanced technologies with a user-first design, this platform revolutionizes the way startups and investors connect--driving impactful interactions and supporting the growth of next-generation businesses. It stands as a powerful tool at the forefront of the startup funding landscape, accelerating innovation and transforming ideas into successful ventures.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Startup-Investor,Next.js,Related Startup,Connect,Founders.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

With the explosive growth of Android applications, mobile malware poses an ever increasing threat to user privacy and device security. Traditional signature based detectors struggle against obfuscated or zero day malware, necessitating intelligent, data driven solutions. This paper presents a lightweight, hybrid Android malware detection framework that combines static feature extraction with an Equilibrium Optimizer (EO)based feature selection module to reduce dimensionality and highlight the most informative attributes. A hybrid ensemble of LightGBM, XGBoost, Random Forest, and a Bidirectional LSTM (Bi-LSTM) model is then employed to classify applications as benign or malicious. The entire pipeline is exposed via a Flask-based REST API, supporting real-time APK uploads, JSON outputs, and SQLite-backed logging. Experimental evaluation on a dataset of 12,000+ APKs achieves an overall accuracy of 95.2%, precision of 94.7%, recall of 95.8%, and F1 score of 95.2%, significantly outperforming baseline methods. The proposed system demonstrates robust detection capabilities, low computational overhead, and easy deploy ability for proactive Android security.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Android malware detection; machine learning; deep learning; Equilibrium Optimizer; hybrid ensemble; real-time scanning.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Smart Voting System through Facial Recognition using OpenCV and Support Vector Machine (SVM) with Histogram of Oriented Gradients (HOG) is an advanced, secure, and efficient voting mechanism designed to enhance the accuracy and reliability of electoral systems. Traditional voting systems are prone to issues such as voter impersonation, voter fraud, and inefficiencies in the verification process. This system utilizes facial recognition technology to address these challenges by ensuring that only eligible voters can cast their votes. The proposed system leverages OpenCV for real-time image processing and feature extraction, with HOG being used for identifying facial features. The facial features are then classified using a Support Vector Machine (SVM) model, trained to differentiate between authorized voters and unauthorized individuals. The SVM classifier is trained on facial data, enabling it to achieve high accuracy and robustness in diverse conditions, such as different lighting or angles of faces. The process begins by capturing the voter's face through a webcam or camera at the voting booth. The facial image is then pre-processed, and HOG descriptors are extracted to capture the shape and structure of the face. The descriptors are subsequently input to the SVM classifier, which compares the facial features with a pre-registered database of authorized voters. If the system matches the captured face with the database, the voter is granted permission to vote. This innovative approach improves the efficiency of the voting process, reduces human error, and significantly increases security by preventing fraud or impersonation. Additionally, the system is cost-effective and scalable, making it a viable solution for both small-scale and large-scale elections.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Support Vector Machine (SVM), Histogram of Oriented Gradients (HOG), OpenCV, Voter Authentication

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Countries that are constantly fighting like India need a well-developed and efficient transport system. Street accidents continue to be one of the leading causes of deaths and injuries around the world. Rapid detection and timely alarm generation are important to reduce death and allow for faster emergency responses. This article presents a real-time accident detection and alarm system that uses image processing and machine learning techniques to automatically identify road accidents from live video feeds. This system is implemented with the Yolov8 algorithm for object recognition (once, version 8). It is trained on two custom datasets. One is for general accident detection (7,512 images), and the other is for fire detection (10,446 images). The proposed model classifies accidents into three categories: car-to-car collisions, single car accidents, and auto brandy. Once recognized, the system immediately belongs to the type of accident to police, hospital, or fire brigade via SMTP or email. Experimental results show that the system is run with high accuracy in real-world scenarios and provides reliable solutions for intelligent monitoring and intelligent transport systems.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Accident detection, YOLOv8, SMTP, alert, Email.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Image caption generation, a primary application domain in computer vision and natural language processing, produces text captions of images from deep learning models. The current paper suggests a CNN-LSTM-based system for automatic captioning, where pre-trained convolutional neural networks (CNNs) are employed for image feature extraction and long short-term memory (LSTM) networks for sequential text generation. Inspired by the Flickr8k dataset, the paper emphasizes primary challenges such as vocabulary sparsity, overfitting, and computational complexity. Experimental results achieve BLEU scores of 0.66 or more, exhibiting coherent caption generation and qualitative analysis discloses captioning inefficiencies for complex scenes. The paper also discusses future enhancements such as transformer-based architectures and attention mechanisms to improve caption accuracy and accessibility. The work contributes to improving large-scale human-computer interaction through multimodal AI systems. Caption generation is an important area at the intersection of computer vision and natural language processing, including the generation of descriptive text captions describing images using advanced deep-learning methodologies. Current paper suggests a new approach through a hybrid CNN-LSTM-based system for automatic captioning. This state-of-the-art model employs pre-trained convolutional neural networks (CNNs) for robust image feature extraction to identify and interpret relevant features in an image. These identified features are then fed to long short-term memory (LSTM) networks adept at generating coherent and relevant sequential text based on the visual input.The experimental results revealed excellent BLEU scores of 0.66 or higher, which reflects the model's capacity to generate captions not only accurate but also linguistically sound. Qualitative analysis of the generated captions does call out inefficiencies in handling complicated scenes with more than one element or activity, and it suggests where there is potential for improvement in the future.In the future, the paper foresees potential enhancements, such as the application of transformer-based models and attention, which would significantly improve caption accuracy and user experience for accessibility. Overall, this work contributes to advancing the state of large-scale human-computer interaction by developing sophisticated multimodal AI systems for interpreting and generating human-like text from visual inputs.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Image captioning, deep learning, CNN, LSTM, attention mechanisms, natural language generation.

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

In life, quitting is often misunderstood as an act of weakness or failure. However, when seen through a philosophical lens, quitting is sometimes not just necessary but essential for growth,liberation, and authentic living. Ancient Indian wisdom, especially the Upanishads,Bhagwat Gita, Yoga Sutra and Writings of various Scholars, teaches that renunciation, detachment, and purposeful quitting are critical to realizing the self and attaining higher states of conscious-ness. Likewise in a world obsessed with noise and motion, choosing silence is an act of courage, a quiet revolution. Within it, the mind breathes, the heart listens, and the soul speaks.This article explores the philosophical as well as Psychological necessity of Quit and Quiet , drawing insights from both existential thought and Philosophical teachings.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Quit, Distress Management, Quite, Philosophy, Bhagwat Gita, Buddhism, Mindfulness,Jainism, Freedom, Mental Health

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Clinical trials are defined as a methodical investigation of a novel medication (therapy regimens, gadgets) in human subjects to provide data for identifying or validating clinical claims or pharmacological and side effects to ascertain the safety and effectiveness of the pharmaceuticals in question.[1]

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

ADR MONITORING AND SAFETY REPORT: AMOXICILLIN

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Mobile phone overuse has emerged as a growing concern, particularly among young individuals, with potential negative effects on brain health and cognitive performance. Brain-Derived Neurotrophic Factor (BDNF), a key neurotrophin involved in synaptic plasticity, learning, and memory, may be affected by excessive mobile usage. This observational study aims to investigate the relationship between BDNF levels and cognitive performance in individuals identified as mobile addicts. The study will include participants aged 18-40 years who meet established criteria for mobile addiction, assessed using Short version of Smartphone Addiction scale. Baseline blood samples will be collected to measure serum BDNF levels using ELISA, and cognitive functions will be evaluated using CogniFit app. The findings may offer valuable insights into the neurobiological consequences of mobile addiction and underscore the need for early interventions.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Mobile addiction, brain derived neurotrophic factor, cognition

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study investigates the effects of globally social media trends on regional environmental graphic design. The study highlights important worldwide trends such as minimalism, bold font, and brilliant colors through the analysis of designer interviews, and quantitative information, and investigates how these styles are integrated into regional design practices. The findings show that, while these global tendencies improve visual appeal and enable recognition globally, they also pose issues such as homogeneity and loss of regional uniqueness. Designers have to tackle these problems by combining global aesthetics with regional cultural settings, assuring both originality and relevance. The study emphasizes the need for additional study into the long-term implications of trend adoption, as well as the significance of new technology in defining future design practices.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Graphic design, Social media trends, Globalization, Regional adaptation, Cultural identity

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

It is often that a crisis situation exposes the hidden psyche of individuals. The paper is an attempt to uncover the complexities that surround the apparent liberal educated middle class in the backdrop of a communal riot situation. Mahesh Dattani weaves the narrative in such a way that the legacy of hatred on communal lines comes to the forefront in the socio-economic political context where the past entwines with the present to shapes an individual's formation of prejudices and false beliefs about a particular community. This is how the process of 'othering' begins at the rudimentary space of the family.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

communal, other, gender, dramaturgy, guilt

  License

Creative Commons Attribution 4.0 and The Open Definition